Projects

The goal of this project is to study whether the neurons in your brain need to be spiking for you to be conscious. Answering this question has major public health consequences for administering anesthesia, treating people with brain injuries, and understanding what happens in the brain during near-death experiences.

In this paper, we first review how IIT’s axiomatic framework has evolved through the latest version (4.0), and why the theory takes such an approach. We then analyze this approach through the lens of Husserlian phenomenology, finding support for both IIT’s axiomatic approach in general, and for all of IIT’s individual axioms.

In this paper, we provide a detailed analysis of IIT’s two silent neuron predictions, showing how they can in fact be tested, contra Bartlett (2022). In the final section of the paper, we also distinguish between two ways of explaining one of the predictions. By drawing on the work of Imre Lakatos, we conclude with a discussion of how the distinction between the two explanations sheds light on why it is so difficult to resolve theoretical disputes about consciousness. Despite these difficulties, we provide a framework that can lead to concrete progress.

In this study, we measure participants’ neurophysiological (EEG), subjective, and behavioral responses in states of normal wakefulness to visual and auditory stimuli that vary in granularity of subjective characteristics, such as meaningfulness. This study advances our understanding of consciousness by clarifying the relationship between stimulus complexity and measures of brain complexity and phenomenology.

In this invited talk at the Society for Brain Mapping and Therapeutics 21st Annual World Congress in Los Angeles, CA, I gave a brief history of consciousness science, reviewed the current state of the neural correlates of consciousness and theories of consciousness, and provided two examples of my ongoing research projects related to integrated information theory.

In this proof-of-concept study, we analyzed three existing EEG datasets where 40 participants performed an active face perception task, an active visual oddball task, and a passive auditory oddball task. We computed the perturbational complexity index (PCIst) and Lempel-ziv complexity (LZc) for every trial and analyzed the results using Bayesian mixed-effects models. We found that i) PCIst was higher for meaningful visual stimuli but that LZc could be higher or lower; i) PCIst was higher for rare visual stimuli but LZc was lower; and ili) PCIst was higher for rare auditory stimuli but LZc did not discriminate rare vs. frequent auditory stimuli.

In this study, we built a binary reservoir network to investigate whether the perturbational complexity index (PCI) peaks near a boundary between order and chaos. We found that PCI exhibits an inverse U-shaped relationship with chaoticity, with values maximal when network dynamics are situated near this critical point, and we present possible explanations for these findings in the context of how PCI is operationalized. Contrary to related work, however, we found that Lempel-Ziv complexity did not exhibit an inverse U-shaped relationship nor peak near a corresponding critical point.

The purpose of this study was to investigate whether the selection of two common EEG reference methods (average-reference and mastoids-reference) had any measurable effects on event-related potentials (ERPs) evoked during a canonical visual oddball experiment. Although it was predicted based on prior studies that a P3 ERP effect would be observed in the target condition for both reference-selection methods, a trending P3 effect was only observed using the mastoids-reference method.